B23-CAL-403 Artificial Intelligence and Expert Systems
| Part A – Introduction | |||
| Subject | BCA (Artificial Intelligence) | ||
| Semester | IV | ||
| Name of the Course | Artificial Intelligence and Expert Systems | ||
| Course Code | B23-CAL-403 | ||
| Course Type: (CC/MCC/MDC/CC- M/DSEC/VOC/DSE/PC/AEC/
VAC) |
CC-C3 | ||
| Level of the course (As per Annexure-I | 200-299 | ||
| Pre-requisite for the course (if any) | Basic understanding of computer systems and programming. | ||
| Course Learning Outcomes(CLO): | After completing this course, the learner will be able to:
1. Understand core principles and techniques of AI and expert systems. 2. Learn and implement AI algorithms and expert system methodologies. 3. Develop practical skills in creating and evaluating expert systems. 4. Discuss ethical considerations and societal impacts of AI and expert systems. 5*. Apply AI techniques to solve real-world problems. |
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| Credits | Theory | Practical | Total |
| 3 | 1 | 4 | |
| Contact Hours | 3 | 2 | 5 |
| Max. Marks:100(70(T)+30(P))
Internal Assessment Marks:30(20(T)+10(P)) End Term Exam Marks: 70(50(T)+20(P)) |
Time: 3 Hrs.(T), 3Hrs.(P) | ||
| Part B- Contents of the Course | |||
| Instructions for Paper-Setter
The examiner will set a total of nine questions. Out of which the first question will be compulsory. The remaining eight questions will be set from four units selecting two questions from each unit. The examination will be of three-hour duration. All questions will carry equal marks. The first question will comprise short answer-type questions covering the entire syllabus. The candidate must attempt five questions, selecting one from each unit. The first question will be compulsory. |
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| The practicum will be evaluated by an external and an internal examiner. The examination will be of three-hour duration. | ||
| Unit | Topics | Contact Hours |
| I | Introduction to Artificial Intelligence: Overview of AI; History; Applications; Terminology
Problem-Solving and Search Algorithms: Problem-solving as search; Uninformed search strategies (BFS, DFS); Informed search strategies (A*) |
11 |
| II | Adversarial Search and Game Playing: Game theory basics; Minimax algorithm; Alpha-beta pruning
Knowledge Representation and Reasoning: Logical agents; Propositional logic; First-order logic; Inference mechanisms |
11 |
| III | Expert Systems Fundamentals: Introduction to expert systems; Components; Knowledge representation; Inference engines
Knowledge Acquisition and Management: Techniques for knowledge acquisition; Knowledge management; Ontologies |
11 |
| IV | Ethics and Social Impacts of AI and Expert Systems: Ethical considerations; Bias and fairness; Societal impact of AI and expert systems
Case Studies in Expert Systems: Case studies of successful expert systems in various domains (e.g., medical diagnosis, financial forecasting) |
12 |
| V* | Practicum:
Students are advised to do laboratory/practical practice not limited to but including the following types of problems: · Exercises on BFS, DFS, and A* algorithms · Implementing Minimax and Alpha-beta pruning algorithms · Exercises on propositional and first-order logic · Exercises on building and implementing rule-based expert systems · Using tools for knowledge management and acquisition · Analyzing case studies and extracting design principles · Group discussions and presentations on case studies · Analysis and discussion of ethical issues related to AI and expert systems |
30 |
| Suggested Evaluation Methods | ||
| Internal Assessment:
➢ Theory · Class Participation: 5 · Seminar/presentation/assignment/quiz/class test etc.: 5 · Mid-Term Exam: 10 ➢ Practicum · Class Participation: NA · Seminar/Demonstration/Viva-voce/Lab records etc.: 10 · Mid-Term Exam: NA |
End-Term Examination: A three-hour exam for both theory and practicum.
End Term Exam Marks: 70(50(T)+20(P )) |
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